{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Robotics Environment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Install MuJoCo\n",
    "\n",
    "1. Download the MuJoCo version 2.1 binaries for\n",
    "   [Linux](https://mujoco.org/download/mujoco210-linux-x86_64.tar.gz) or\n",
    "   [OSX](https://mujoco.org/download/mujoco210-macos-x86_64.tar.gz).\n",
    "2. Extract the downloaded `mujoco210` directory into `~/.mujoco/mujoco210`.\n",
    "\n",
    "You can read more about it [here](https://github.com/openai/mujoco-py/blob/master/README.md)\n",
    "\n",
    "\n",
    "Please add following line to `.bashrc`:\n",
    "`export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/nsanghi/.mujoco/mujoco210/bin`\n",
    "\n",
    "If you get error - you may need to run following commands:\n",
    "\n",
    "\n",
    "`sudo apt-get install libosmesa6-dev`\n",
    "\n",
    "`sudo apt-get install patchelf`\n",
    "\n",
    "### Note for Macbook with arm 64 architecture chipsets (m1/m2/m3 chipsets) trying to run this notebook directly on a local install\n",
    "As of writing the book, Mujoco is not supported for your architecture.  \n",
    "If you are still interested to pursue that route you may want to take a look at a github thread for more guidance:\n",
    "https://github.com/google-deepmind/mujoco/issues/882\n",
    "\n",
    "The other option is to use Rosetta on your Mac and then remove/install Homebrew for the intel architecture. After these steps, you should be able to install mujoco\n",
    "\n",
    "**Having said that, easier and safer option would be to use VSCode devcontainer setup. Please refer to the the `istallation_step.pdf` in root directory for more details.** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running in Colab/Kaggle\n",
    "\n",
    "If you are running this on Colab, please uncomment below cells and run this to install required dependencies.\n",
    "\n",
    "**Please note that this notebook requires additonal installation as explained above. Please follow the additional install process making tweaks, if necessary, as per restrictions in your cloud environment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "## uncomment and execute this cell to install all the the dependencies if running in Google Colab or Kaggle\n",
    "# !apt-get update \n",
    "# !apt-get install -y swig cmake ffmpeg freeglut3-dev xvfb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Uncomment and execute this cell to install all the the dependencies if running in Google Colab or Kaggle\n",
    "\n",
    "## Uncomment and run for Colab\n",
    "# !git clone https://github.com/nsanghi/drl-2ed\n",
    "# %cd /content/drl-2ed \n",
    "# !pip install  -r requirements.txt\n",
    "# %cd chapter6\n",
    "\n",
    "\n",
    "## Uncomment and run for Kaggle\n",
    "# !git clone https://github.com/nsanghi/drl-2ed\n",
    "# %cd /kaggle/working/drl-2ed \n",
    "# !pip install  -r requirements.txt\n",
    "# %cd chapter6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Uncomment and Rerun the cd command in case you were asked to restart the kernel and you followed that message\n",
    "## as after resart the kernel will again point back to root folder\n",
    "\n",
    "\n",
    "## Uncomment and run for Colab\n",
    "# %cd /content/drl-2ed \n",
    "# %cd chapter6\n",
    "\n",
    "\n",
    "## Uncomment and run for Kaggle\n",
    "# %cd /kaggle/working/drl-2ed \n",
    "# %cd chapter6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-05 23:05:00.498122: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-11-05 23:05:00.500980: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-05 23:05:00.535813: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-05 23:05:00.535851: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-05 23:05:00.535869: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-11-05 23:05:00.542517: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-05 23:05:00.542922: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-05 23:05:01.731125: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from base64 import b64encode\n",
    "from IPython.display import HTML\n",
    "from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv\n",
    "\n",
    "\n",
    "def play_video(file_path):\n",
    "    mp4 = open(file_path, 'rb').read()\n",
    "    data_url = \"data:video/mp4;base64,\" + b64encode(mp4).decode()\n",
    "    return HTML(\"\"\"\n",
    "        <video width=400 controls>\n",
    "              <source src=\"%s\" type=\"video/mp4\">\n",
    "        </video>\n",
    "        \"\"\" % data_url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Login to Weights and Biases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnsanghi\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT**\n",
    "In case you face a login error with `wandb.login()`, please run the following cell by setting first your wandb api key"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"WANDB_API_KEY\"] = \"Your WANDB_API_KEY......\"\n",
    "import wandb\n",
    "wandb.login()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{python}\n",
    "hyperparams = {\n",
    "    \"Reacher-v4\": dict(\n",
    "        normalize=False,\n",
    "        n_envs=1,\n",
    "        policy=\"MlpPolicy\",\n",
    "        n_timesteps=100000.0,\n",
    "        batch_size=32,\n",
    "        n_steps=512,\n",
    "        gamma=0.9,\n",
    "        learning_rate=0.000104019,\n",
    "        ent_coef=7.52585e-08,\n",
    "        clip_range=0.3,\n",
    "        n_epochs=5,\n",
    "        gae_lambda=1.0,\n",
    "        max_grad_norm=0.9,\n",
    "        vf_coef=0.950368\n",
    "    )\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Reacher MuJoCo using RL Zoo3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-06 01:17:16.432035: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-11-06 01:17:16.434352: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-06 01:17:16.469920: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-06 01:17:16.470017: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-06 01:17:16.470041: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-11-06 01:17:16.477040: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-06 01:17:16.477331: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-06 01:17:17.296151: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "========== Reacher-v4 ==========\n",
      "Seed: 2709960376\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnsanghi\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.12\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/home/nsanghi/sandbox/apress/drl-2ed/chapter6/wandb/run-20231106_011721-isjrthv7\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mReacher-v4__ppo__2709960376__1699213640\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/nsanghi/ppo-reacher-v4\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/nsanghi/ppo-reacher-v4/runs/isjrthv7\u001b[0m\n",
      "Loading hyperparameters from: ppo_config_6e.py\n",
      "Default hyperparameters for environment (ones being tuned will be overridden):\n",
      "OrderedDict([('batch_size', 32),\n",
      "             ('clip_range', 0.3),\n",
      "             ('ent_coef', 7.52585e-08),\n",
      "             ('gae_lambda', 1.0),\n",
      "             ('gamma', 0.9),\n",
      "             ('learning_rate', 0.000104019),\n",
      "             ('max_grad_norm', 0.9),\n",
      "             ('n_envs', 1),\n",
      "             ('n_epochs', 5),\n",
      "             ('n_steps', 512),\n",
      "             ('n_timesteps', 100000.0),\n",
      "             ('normalize', False),\n",
      "             ('policy', 'MlpPolicy'),\n",
      "             ('vf_coef', 0.950368)])\n",
      "Using 1 environments\n",
      "Overwriting n_timesteps with n=100000\n",
      "Creating test environment\n",
      "Using cpu device\n",
      "Log path: logs/6_e/rlzoo3//ppo/Reacher-v4_1\n",
      "Logging to runs/Reacher-v4__ppo__2709960376__1699213640/Reacher-v4/PPO_1\n",
      "\u001b[2KEval num_timesteps=10000, episode_reward=-13.02 +/- 4.20━━━━━━━━\u001b[0m \u001b[32m9,887/100,000 \u001b[0m [ \u001b[33m0:00:14\u001b[0m < \u001b[36m0:02:12\u001b[0m , \u001b[31m687 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.000m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9,887/100,000 \u001b[0m [ \u001b[33m0:00:14\u001b[0m < \u001b[36m0:02:12\u001b[0m , \u001b[31m687 it/s\u001b[0m ]\n",
      "\u001b[2K-----------------------------------------━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9,887/100,000 \u001b[0m [ \u001b[33m0:00:14\u001b[0m < \u001b[36m0:02:12\u001b[0m , \u001b[31m687 it/s\u001b[0m ]\n",
      "| eval/                   |             |\n",
      "|    mean_ep_length       | 50          |\n",
      "|    mean_reward          | -13         |\n",
      "| time/                   |             |\n",
      "|    total_timesteps      | 10000       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.001352274 |\n",
      "|    clip_fraction        | 0           |\n",
      "|    clip_range           | 0.3         |\n",
      "|    entropy_loss         | -2.74       |\n",
      "|    explained_variance   | -0.637      |\n",
      "|    learning_rate        | 0.000104    |\n",
      "|    loss                 | 21.8        |\n",
      "|    n_updates            | 95          |\n",
      "|    policy_gradient_loss | -0.00284    |\n",
      "|    std                  | 0.951       |\n",
      "|    value_loss           | 30.2        |\n",
      "-----------------------------------------\n",
      "\u001b[2KNew best mean reward!m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9,887/100,000 \u001b[0m [ \u001b[33m0:00:14\u001b[0m < \u001b[36m0:02:12\u001b[0m , \u001b[31m687 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=20000, episode_reward=-9.84 +/- 3.16━━━━━━━━\u001b[0m \u001b[32m19,969/100,000 \u001b[0m [ \u001b[33m0:00:30\u001b[0m < \u001b[36m0:02:03\u001b[0m , \u001b[31m652 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.00\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19,969/100,000 \u001b[0m [ \u001b[33m0:00:30\u001b[0m < \u001b[36m0:02:03\u001b[0m , \u001b[31m652 it/s\u001b[0m ]\n",
      "\u001b[2K------------------------------------------━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19,969/100,000 \u001b[0m [ \u001b[33m0:00:30\u001b[0m < \u001b[36m0:02:03\u001b[0m , \u001b[31m652 it/s\u001b[0m ]\n",
      "| eval/                   |              |\n",
      "|    mean_ep_length       | 50           |\n",
      "|    mean_reward          | -9.84        |\n",
      "| time/                   |              |\n",
      "|    total_timesteps      | 20000        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0011662848 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.3          |\n",
      "|    entropy_loss         | -2.47        |\n",
      "|    explained_variance   | -0.279       |\n",
      "|    learning_rate        | 0.000104     |\n",
      "|    loss                 | 1.5          |\n",
      "|    n_updates            | 195          |\n",
      "|    policy_gradient_loss | -0.00367     |\n",
      "|    std                  | 0.828        |\n",
      "|    value_loss           | 2.21         |\n",
      "------------------------------------------\n",
      "\u001b[2KNew best mean reward![0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19,969/100,000 \u001b[0m [ \u001b[33m0:00:30\u001b[0m < \u001b[36m0:02:03\u001b[0m , \u001b[31m652 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=30000, episode_reward=-10.50 +/- 2.18━━━━━━━\u001b[0m \u001b[32m29,853/100,000 \u001b[0m [ \u001b[33m0:00:45\u001b[0m < \u001b[36m0:01:52\u001b[0m , \u001b[31m630 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.000m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29,853/100,000 \u001b[0m [ \u001b[33m0:00:45\u001b[0m < \u001b[36m0:01:52\u001b[0m , \u001b[31m630 it/s\u001b[0m ]\n",
      "\u001b[2K-----------------------------------------m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29,853/100,000 \u001b[0m [ \u001b[33m0:00:45\u001b[0m < \u001b[36m0:01:52\u001b[0m , \u001b[31m630 it/s\u001b[0m ]\n",
      "| eval/                   |             |\n",
      "|    mean_ep_length       | 50          |\n",
      "|    mean_reward          | -10.5       |\n",
      "| time/                   |             |\n",
      "|    total_timesteps      | 30000       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.002034219 |\n",
      "|    clip_fraction        | 0           |\n",
      "|    clip_range           | 0.3         |\n",
      "|    entropy_loss         | -2.2        |\n",
      "|    explained_variance   | 0.101       |\n",
      "|    learning_rate        | 0.000104    |\n",
      "|    loss                 | 1.1         |\n",
      "|    n_updates            | 290         |\n",
      "|    policy_gradient_loss | -0.0045     |\n",
      "|    std                  | 0.723       |\n",
      "|    value_loss           | 1.24        |\n",
      "-----------------------------------------\n",
      "\u001b[2KEval num_timesteps=40000, episode_reward=-9.38 +/- 2.51━━━━━━━━\u001b[0m \u001b[32m39,928/100,000 \u001b[0m [ \u001b[33m0:01:00\u001b[0m < \u001b[36m0:01:31\u001b[0m , \u001b[31m667 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.00[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39,928/100,000 \u001b[0m [ \u001b[33m0:01:00\u001b[0m < \u001b[36m0:01:31\u001b[0m , \u001b[31m667 it/s\u001b[0m ]\n",
      "\u001b[2K------------------------------------------0m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39,928/100,000 \u001b[0m [ \u001b[33m0:01:00\u001b[0m < \u001b[36m0:01:31\u001b[0m , \u001b[31m667 it/s\u001b[0m ]\n",
      "| eval/                   |              |\n",
      "|    mean_ep_length       | 50           |\n",
      "|    mean_reward          | -9.38        |\n",
      "| time/                   |              |\n",
      "|    total_timesteps      | 40000        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0018890968 |\n",
      "|    clip_fraction        | 0            |\n",
      "|    clip_range           | 0.3          |\n",
      "|    entropy_loss         | -1.93        |\n",
      "|    explained_variance   | 0.042        |\n",
      "|    learning_rate        | 0.000104     |\n",
      "|    loss                 | 1.55         |\n",
      "|    n_updates            | 390          |\n",
      "|    policy_gradient_loss | -0.00486     |\n",
      "|    std                  | 0.633        |\n",
      "|    value_loss           | 1.6          |\n",
      "------------------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39,928/100,000 \u001b[0m [ \u001b[33m0:01:00\u001b[0m < \u001b[36m0:01:31\u001b[0m , \u001b[31m667 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=50000, episode_reward=-8.77 +/- 3.02━━━━━━━━\u001b[0m \u001b[32m49,982/100,000 \u001b[0m [ \u001b[33m0:01:15\u001b[0m < \u001b[36m0:01:14\u001b[0m , \u001b[31m682 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.000m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m49,982/100,000 \u001b[0m [ \u001b[33m0:01:15\u001b[0m < \u001b[36m0:01:14\u001b[0m , \u001b[31m682 it/s\u001b[0m ]\n",
      "\u001b[2K------------------------------------------\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m49,982/100,000 \u001b[0m [ \u001b[33m0:01:15\u001b[0m < \u001b[36m0:01:14\u001b[0m , \u001b[31m682 it/s\u001b[0m ]\n",
      "| eval/                   |              |\n",
      "|    mean_ep_length       | 50           |\n",
      "|    mean_reward          | -8.77        |\n",
      "| time/                   |              |\n",
      "|    total_timesteps      | 50000        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0033457302 |\n",
      "|    clip_fraction        | 0.000781     |\n",
      "|    clip_range           | 0.3          |\n",
      "|    entropy_loss         | -1.68        |\n",
      "|    explained_variance   | 0.117        |\n",
      "|    learning_rate        | 0.000104     |\n",
      "|    loss                 | 0.871        |\n",
      "|    n_updates            | 485          |\n",
      "|    policy_gradient_loss | -0.00692     |\n",
      "|    std                  | 0.558        |\n",
      "|    value_loss           | 0.944        |\n",
      "------------------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m49,982/100,000 \u001b[0m [ \u001b[33m0:01:15\u001b[0m < \u001b[36m0:01:14\u001b[0m , \u001b[31m682 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=60000, episode_reward=-9.25 +/- 2.69━━━━━━━━\u001b[0m \u001b[32m59,955/100,000 \u001b[0m [ \u001b[33m0:01:33\u001b[0m < \u001b[36m0:01:06\u001b[0m , \u001b[31m607 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.00\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m59,955/100,000 \u001b[0m [ \u001b[33m0:01:33\u001b[0m < \u001b[36m0:01:06\u001b[0m , \u001b[31m607 it/s\u001b[0m ]\n",
      "\u001b[2K-----------------------------------------[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m59,955/100,000 \u001b[0m [ \u001b[33m0:01:33\u001b[0m < \u001b[36m0:01:06\u001b[0m , \u001b[31m607 it/s\u001b[0m ]\n",
      "| eval/                   |             |\n",
      "|    mean_ep_length       | 50          |\n",
      "|    mean_reward          | -9.25       |\n",
      "| time/                   |             |\n",
      "|    total_timesteps      | 60000       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.001877877 |\n",
      "|    clip_fraction        | 0           |\n",
      "|    clip_range           | 0.3         |\n",
      "|    entropy_loss         | -1.42       |\n",
      "|    explained_variance   | 0.0387      |\n",
      "|    learning_rate        | 0.000104    |\n",
      "|    loss                 | 0.29        |\n",
      "|    n_updates            | 585         |\n",
      "|    policy_gradient_loss | -0.00592    |\n",
      "|    std                  | 0.489       |\n",
      "|    value_loss           | 0.743       |\n",
      "-----------------------------------------\n",
      "\u001b[2KEval num_timesteps=70000, episode_reward=-10.76 +/- 3.10━━━━━━━\u001b[0m \u001b[32m69,949/100,000 \u001b[0m [ \u001b[33m0:01:52\u001b[0m < \u001b[36m0:00:56\u001b[0m , \u001b[31m544 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.00━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m69,949/100,000 \u001b[0m [ \u001b[33m0:01:52\u001b[0m < \u001b[36m0:00:56\u001b[0m , \u001b[31m544 it/s\u001b[0m ]\n",
      "\u001b[2K------------------------------------------╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m69,949/100,000 \u001b[0m [ \u001b[33m0:01:52\u001b[0m < \u001b[36m0:00:56\u001b[0m , \u001b[31m544 it/s\u001b[0m ]\n",
      "| eval/                   |              |\n",
      "|    mean_ep_length       | 50           |\n",
      "|    mean_reward          | -10.8        |\n",
      "| time/                   |              |\n",
      "|    total_timesteps      | 70000        |\n",
      "| train/                  |              |\n",
      "|    approx_kl            | 0.0066577806 |\n",
      "|    clip_fraction        | 0.00508      |\n",
      "|    clip_range           | 0.3          |\n",
      "|    entropy_loss         | -1.16        |\n",
      "|    explained_variance   | 0.147        |\n",
      "|    learning_rate        | 0.000104     |\n",
      "|    loss                 | 0.342        |\n",
      "|    n_updates            | 680          |\n",
      "|    policy_gradient_loss | -0.0057      |\n",
      "|    std                  | 0.43         |\n",
      "|    value_loss           | 0.563        |\n",
      "------------------------------------------\n",
      "\u001b[2KEval num_timesteps=80000, episode_reward=-12.55 +/- 1.560m━━━━━\u001b[0m \u001b[32m79,891/100,000 \u001b[0m [ \u001b[33m0:02:07\u001b[0m < \u001b[36m0:00:33\u001b[0m , \u001b[31m626 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.00━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m79,891/100,000 \u001b[0m [ \u001b[33m0:02:07\u001b[0m < \u001b[36m0:00:33\u001b[0m , \u001b[31m626 it/s\u001b[0m ]\n",
      "\u001b[2K-----------------------------------------91m╸\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m79,891/100,000 \u001b[0m [ \u001b[33m0:02:07\u001b[0m < \u001b[36m0:00:33\u001b[0m , \u001b[31m626 it/s\u001b[0m ]\n",
      "| eval/                   |             |\n",
      "|    mean_ep_length       | 50          |\n",
      "|    mean_reward          | -12.5       |\n",
      "| time/                   |             |\n",
      "|    total_timesteps      | 80000       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.013218039 |\n",
      "|    clip_fraction        | 0.0242      |\n",
      "|    clip_range           | 0.3         |\n",
      "|    entropy_loss         | -0.892      |\n",
      "|    explained_variance   | 0.399       |\n",
      "|    learning_rate        | 0.000104    |\n",
      "|    loss                 | 0.328       |\n",
      "|    n_updates            | 780         |\n",
      "|    policy_gradient_loss | -0.00867    |\n",
      "|    std                  | 0.377       |\n",
      "|    value_loss           | 0.463       |\n",
      "-----------------------------------------\n",
      "\u001b[2KEval num_timesteps=90000, episode_reward=-8.62 +/- 3.77m\u001b[90m━━\u001b[0m \u001b[32m89,910/100,000 \u001b[0m [ \u001b[33m0:02:23\u001b[0m < \u001b[36m0:00:16\u001b[0m , \u001b[31m662 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.00━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m89,910/100,000 \u001b[0m [ \u001b[33m0:02:23\u001b[0m < \u001b[36m0:00:16\u001b[0m , \u001b[31m662 it/s\u001b[0m ]\n",
      "\u001b[2K-----------------------------------------m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m89,910/100,000 \u001b[0m [ \u001b[33m0:02:23\u001b[0m < \u001b[36m0:00:16\u001b[0m , \u001b[31m662 it/s\u001b[0m ]\n",
      "| eval/                   |             |\n",
      "|    mean_ep_length       | 50          |\n",
      "|    mean_reward          | -8.62       |\n",
      "| time/                   |             |\n",
      "|    total_timesteps      | 90000       |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.010185342 |\n",
      "|    clip_fraction        | 0.00977     |\n",
      "|    clip_range           | 0.3         |\n",
      "|    entropy_loss         | -0.65       |\n",
      "|    explained_variance   | 0.44        |\n",
      "|    learning_rate        | 0.000104    |\n",
      "|    loss                 | 0.231       |\n",
      "|    n_updates            | 875         |\n",
      "|    policy_gradient_loss | -0.00847    |\n",
      "|    std                  | 0.334       |\n",
      "|    value_loss           | 0.345       |\n",
      "-----------------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m89,910/100,000 \u001b[0m [ \u001b[33m0:02:23\u001b[0m < \u001b[36m0:00:16\u001b[0m , \u001b[31m662 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=100000, episode_reward=-8.73 +/- 2.240m \u001b[32m99,997/100,000 \u001b[0m [ \u001b[33m0:02:38\u001b[0m < \u001b[36m0:00:01\u001b[0m , \u001b[31m643 it/s\u001b[0m ]t/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 50.00 +/- 0.00━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m99,997/100,000 \u001b[0m [ \u001b[33m0:02:38\u001b[0m < \u001b[36m0:00:01\u001b[0m , \u001b[31m643 it/s\u001b[0m ]\n",
      "\u001b[2K-----------------------------------------[0m\u001b[91m╸\u001b[0m \u001b[32m99,997/100,000 \u001b[0m [ \u001b[33m0:02:38\u001b[0m < \u001b[36m0:00:01\u001b[0m , \u001b[31m643 it/s\u001b[0m ]\n",
      "| eval/                   |             |\n",
      "|    mean_ep_length       | 50          |\n",
      "|    mean_reward          | -8.73       |\n",
      "| time/                   |             |\n",
      "|    total_timesteps      | 100000      |\n",
      "| train/                  |             |\n",
      "|    approx_kl            | 0.010235589 |\n",
      "|    clip_fraction        | 0.0164      |\n",
      "|    clip_range           | 0.3         |\n",
      "|    entropy_loss         | -0.42       |\n",
      "|    explained_variance   | 0.0995      |\n",
      "|    learning_rate        | 0.000104    |\n",
      "|    loss                 | 0.261       |\n",
      "|    n_updates            | 975         |\n",
      "|    policy_gradient_loss | -0.00694    |\n",
      "|    std                  | 0.298       |\n",
      "|    value_loss           | 0.372       |\n",
      "-----------------------------------------\n",
      "\u001b[2K\u001b[35m 100%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100,352/100,000 \u001b[0m [ \u001b[33m0:02:38\u001b[0m < \u001b[36m0:00:00\u001b[0m , \u001b[31m638 it/s\u001b[0m ]t/s\u001b[0m ]\n",
      "\u001b[?25hSaving to logs/6_e/rlzoo3//ppo/Reacher-v4_1\n"
     ]
    }
   ],
   "source": [
    "!python -m rl_zoo3.train --algo ppo --env Reacher-v4 --progress \\\n",
    "--log-interval 400 --save-freq 10000 --eval-freq 10000 --eval-episodes 10 \\\n",
    "--n-timesteps 100000 --conf-file ppo_config_6e.py \\\n",
    "--track --wandb-project-name ppo-reacher-v4 -f logs/6_d/rlzoo3/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Evaluate trained agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-06 00:16:24.073646: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-11-06 00:16:24.075927: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-06 00:16:24.110042: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-06 00:16:24.110117: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-06 00:16:24.110142: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-11-06 00:16:24.116651: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-06 00:16:24.116917: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-06 00:16:24.799618: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "Loading latest experiment, id=2\n",
      "Loading logs/6_e/rlzoo3/ppo/Reacher-v4_2/Reacher-v4.zip\n",
      "Loading running average\n",
      "with params: {'norm_obs': True, 'norm_reward': False}\n",
      "Episode Reward: -5.84\n",
      "Episode Length 50\n",
      "Episode Reward: -4.39\n",
      "Episode Length 50\n",
      "Episode Reward: -5.43\n",
      "Episode Length 50\n",
      "Episode Reward: -1.77\n",
      "Episode Length 50\n",
      "Episode Reward: -4.80\n",
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      "Episode Reward: -6.89\n",
      "Episode Length 50\n",
      "Episode Reward: -7.60\n",
      "Episode Length 50\n",
      "Episode Reward: -4.83\n",
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      "Episode Reward: -6.10\n",
      "Episode Length 50\n",
      "Episode Reward: -6.28\n",
      "Episode Length 50\n",
      "Episode Reward: -8.48\n",
      "Episode Length 50\n",
      "Episode Reward: -7.52\n",
      "Episode Length 50\n",
      "Episode Reward: -5.68\n",
      "Episode Length 50\n",
      "Episode Reward: -7.30\n",
      "Episode Length 50\n",
      "Episode Reward: -8.37\n",
      "Episode Length 50\n",
      "Episode Reward: -7.42\n",
      "Episode Length 50\n",
      "Episode Reward: -5.79\n",
      "Episode Length 50\n",
      "Episode Reward: -7.72\n",
      "Episode Length 50\n",
      "Episode Reward: -5.41\n",
      "Episode Length 50\n",
      "Episode Reward: -7.99\n",
      "Episode Length 50\n",
      "Episode Reward: -6.27\n",
      "Episode Length 50\n",
      "Episode Reward: -5.95\n",
      "Episode Length 50\n",
      "Episode Reward: -11.15\n",
      "Episode Length 50\n",
      "Episode Reward: -6.49\n",
      "Episode Length 50\n",
      "Episode Reward: -9.78\n",
      "Episode Length 50\n",
      "Episode Reward: -6.86\n",
      "Episode Length 50\n",
      "Episode Reward: -5.58\n",
      "Episode Length 50\n",
      "Episode Reward: -7.07\n",
      "Episode Length 50\n",
      "100 Episodes\n",
      "Mean reward: -7.71 +/- 2.83\n",
      "Mean episode length: 50.00 +/- 0.00\n"
     ]
    }
   ],
   "source": [
    "!python -m rl_zoo3.enjoy --algo ppo --env Reacher-v4 --no-render --n-timesteps 5000 --folder logs/6_d/rlzoo3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Record Video and play back"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-06 01:23:28.354226: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-11-06 01:23:28.356701: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-06 01:23:28.395988: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-06 01:23:28.396072: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-06 01:23:28.396103: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-11-06 01:23:28.404204: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-06 01:23:28.404510: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-06 01:23:29.582817: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "Loading latest experiment, id=1\n",
      "Loading logs/6_e/rlzoo3/ppo/Reacher-v4_1/Reacher-v4.zip\n",
      "Loading logs/6_e/rlzoo3/ppo/Reacher-v4_1/Reacher-v4.zip\n",
      "Saving video to /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/6_e/rlzoo3/ppo/Reacher-v4_1/videos/final-model-ppo-Reacher-v4-step-0-to-step-1000.mp4\n",
      "Moviepy - Building video /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/6_e/rlzoo3/ppo/Reacher-v4_1/videos/final-model-ppo-Reacher-v4-step-0-to-step-1000.mp4.\n",
      "Moviepy - Writing video /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/6_e/rlzoo3/ppo/Reacher-v4_1/videos/final-model-ppo-Reacher-v4-step-0-to-step-1000.mp4\n",
      "\n",
      "Moviepy - Done !                                                                \n",
      "Moviepy - video ready /home/nsanghi/sandbox/apress/drl-2ed/chapter6/logs/6_e/rlzoo3/ppo/Reacher-v4_1/videos/final-model-ppo-Reacher-v4-step-0-to-step-1000.mp4\n"
     ]
    }
   ],
   "source": [
    "!python -m rl_zoo3.record_video --algo ppo --env Reacher-v4 --exp-id 0 -f logs/6_d/rlzoo3/ -n 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <video width=400 controls>\n",
       "              <source 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type=\"video/mp4\">\n",
       "        </video>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "video_file_rlzoo = \"logs/6_d/rlzoo3/ppo/Reacher-v4_1/videos/final-model-ppo-Reacher-v4-step-0-to-step-1000.mp4\"\n",
    "play_video(video_file_rlzoo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Train Agent with TRPO, a different Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-11-06 01:34:40.589819: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2023-11-06 01:34:40.592202: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-06 01:34:40.628660: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2023-11-06 01:34:40.628737: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2023-11-06 01:34:40.628762: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2023-11-06 01:34:40.636171: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "2023-11-06 01:34:40.636433: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-06 01:34:41.331144: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
      "========== ReacherBulletEnv-v0 ==========\n",
      "Seed: 2138361651\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mnsanghi\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.15.12\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/home/nsanghi/sandbox/apress/drl-2ed/chapter6/wandb/run-20231106_013445-spchp2lq\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mReacherBulletEnv-v0__trpo__2138361651__1699214684\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/nsanghi/trpo-ReacherBulletEnv-v0\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/nsanghi/trpo-ReacherBulletEnv-v0/runs/spchp2lq\u001b[0m\n",
      "Loading hyperparameters from: /home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/rl_zoo3/hyperparams/trpo.yml\n",
      "Default hyperparameters for environment (ones being tuned will be overridden):\n",
      "OrderedDict([('batch_size', 128),\n",
      "             ('cg_damping', 0.1),\n",
      "             ('cg_max_steps', 25),\n",
      "             ('gae_lambda', 0.95),\n",
      "             ('gamma', 0.99),\n",
      "             ('learning_rate', 0.001),\n",
      "             ('n_critic_updates', 20),\n",
      "             ('n_envs', 2),\n",
      "             ('n_steps', 1024),\n",
      "             ('n_timesteps', 300000.0),\n",
      "             ('normalize', True),\n",
      "             ('policy', 'MlpPolicy'),\n",
      "             ('policy_kwargs',\n",
      "              'dict(log_std_init=-1, ortho_init=False, activation_fn=nn.ReLU, '\n",
      "              'net_arch=dict(pi=[256, 256], vf=[256, 256]) )'),\n",
      "             ('sub_sampling_factor', 1)])\n",
      "Using 2 environments\n",
      "Overwriting n_timesteps with n=100000\n",
      "Creating test environment\n",
      "pybullet build time: May 20 2022 19:45:31\n",
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/stable_baselines3/common/vec_env/patch_gym.py:49: UserWarning: You provided an OpenAI Gym environment. We strongly recommend transitioning to Gymnasium environments. Stable-Baselines3 is automatically wrapping your environments in a compatibility layer, which could potentially cause issues.\n",
      "  warnings.warn(\n",
      "Normalization activated: {'gamma': 0.99, 'norm_reward': False, 'training': False}\n",
      "Normalization activated: {'gamma': 0.99}\n",
      "Using cpu device\n",
      "Log path: logs/6_e/rlzoo3//trpo/ReacherBulletEnv-v0_1\n",
      "argv[0]=\n",
      "argv[0]=\n",
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gym/utils/passive_env_checker.py:141: UserWarning: \u001b[33mWARN: The obs returned by the `reset()` method was expecting numpy array dtype to be float32, actual type: float64\u001b[0m\n",
      "  logger.warn(\n",
      "/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gym/utils/passive_env_checker.py:165: UserWarning: \u001b[33mWARN: The obs returned by the `reset()` method is not within the observation space.\u001b[0m\n",
      "  logger.warn(f\"{pre} is not within the observation space.\")\n",
      "argv[0]=\n",
      "argv[0]=\n",
      "Logging to runs/ReacherBulletEnv-v0__trpo__2138361651__1699214684/ReacherBulletEnv-v0/TRPO_1\n",
      "\u001b[2K/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gym/util[33m0:00:00\u001b[0m < \u001b[36m-:--:--\u001b[0m , \u001b[31m? it/s\u001b[0m ]\n",
      "s/passive_env_checker.py:141: UserWarning: \u001b[33mWARN: The obs returned by the \u001b[0m\n",
      "\u001b[33m`step()` method was expecting numpy array dtype to be float32, actual type: \u001b[0m\n",
      "\u001b[33mfloat64\u001b[0m\n",
      "  logger.warn(\n",
      "\u001b[2K/home/nsanghi/sandbox/apress/drl-2ed/venv39/lib/python3.9/site-packages/gym/util0:00:00\u001b[0m < \u001b[36m-:--:--\u001b[0m , \u001b[31m? it/s\u001b[0m ]\n",
      "s/passive_env_checker.py:165: UserWarning: \u001b[33mWARN: The obs returned by the \u001b[0m\n",
      "\u001b[33m`step()` method is not within the observation space.\u001b[0m\n",
      "  logger.warn(f\"{pre} is not within the observation space.\")\n",
      "\u001b[2K\u001b[35m  10%\u001b[0m \u001b[91m━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9,966/100,000 \u001b[0m [ \u001b[33m0:00:23\u001b[0m < \u001b[36m0:03:34\u001b[0m , \u001b[31m421 it/s\u001b[0m ]argv[0]=\n",
      "argv[0]=\n",
      "\u001b[2KEval num_timesteps=10000, episode_reward=-0.69 +/- 12.03━━━━━━━━\u001b[0m \u001b[32m9,966/100,000 \u001b[0m [ \u001b[33m0:00:25\u001b[0m < \u001b[36m0:03:34\u001b[0m , \u001b[31m421 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9,966/100,000 \u001b[0m [ \u001b[33m0:00:25\u001b[0m < \u001b[36m0:03:34\u001b[0m , \u001b[31m421 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9,966/100,000 \u001b[0m [ \u001b[33m0:00:25\u001b[0m < \u001b[36m0:03:34\u001b[0m , \u001b[31m421 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | -0.687   |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 10000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.865    |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00406  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 4        |\n",
      "|    policy_objective       | 0.0417   |\n",
      "|    std                    | 0.368    |\n",
      "|    value_loss             | 0.0121   |\n",
      "----------------------------------------\n",
      "\u001b[2KNew best mean reward!m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9,966/100,000 \u001b[0m [ \u001b[33m0:00:25\u001b[0m < \u001b[36m0:03:34\u001b[0m , \u001b[31m421 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=20000, episode_reward=-3.31 +/- 14.04━━━━━━━\u001b[0m \u001b[32m19,910/100,000 \u001b[0m [ \u001b[33m0:00:55\u001b[0m < \u001b[36m0:03:49\u001b[0m , \u001b[31m350 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19,910/100,000 \u001b[0m [ \u001b[33m0:00:55\u001b[0m < \u001b[36m0:03:49\u001b[0m , \u001b[31m350 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19,910/100,000 \u001b[0m [ \u001b[33m0:00:55\u001b[0m < \u001b[36m0:03:49\u001b[0m , \u001b[31m350 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | -3.31    |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 20000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.958    |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00497  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 9        |\n",
      "|    policy_objective       | 0.0531   |\n",
      "|    std                    | 0.353    |\n",
      "|    value_loss             | 0.00425  |\n",
      "----------------------------------------\n",
      "\u001b[2KEval num_timesteps=30000, episode_reward=3.63 +/- 14.77━━━━━━━━\u001b[0m \u001b[32m29,912/100,000 \u001b[0m [ \u001b[33m0:01:25\u001b[0m < \u001b[36m0:03:26\u001b[0m , \u001b[31m341 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29,912/100,000 \u001b[0m [ \u001b[33m0:01:25\u001b[0m < \u001b[36m0:03:26\u001b[0m , \u001b[31m341 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------0m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29,912/100,000 \u001b[0m [ \u001b[33m0:01:25\u001b[0m < \u001b[36m0:03:26\u001b[0m , \u001b[31m341 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | 3.63     |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 30000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.961    |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00476  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 14       |\n",
      "|    policy_objective       | 0.0474   |\n",
      "|    std                    | 0.34     |\n",
      "|    value_loss             | 0.00315  |\n",
      "----------------------------------------\n",
      "\u001b[2KNew best mean reward!━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29,912/100,000 \u001b[0m [ \u001b[33m0:01:25\u001b[0m < \u001b[36m0:03:26\u001b[0m , \u001b[31m341 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=40000, episode_reward=3.58 +/- 11.52━━━━━━━━\u001b[0m \u001b[32m39,992/100,000 \u001b[0m [ \u001b[33m0:02:00\u001b[0m < \u001b[36m0:02:55\u001b[0m , \u001b[31m345 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.0091m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39,992/100,000 \u001b[0m [ \u001b[33m0:02:00\u001b[0m < \u001b[36m0:02:55\u001b[0m , \u001b[31m345 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39,992/100,000 \u001b[0m [ \u001b[33m0:02:00\u001b[0m < \u001b[36m0:02:55\u001b[0m , \u001b[31m345 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | 3.58     |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 40000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.947    |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00467  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 19       |\n",
      "|    policy_objective       | 0.0469   |\n",
      "|    std                    | 0.332    |\n",
      "|    value_loss             | 0.00313  |\n",
      "----------------------------------------\n",
      "\u001b[2KEval num_timesteps=50000, episode_reward=2.54 +/- 7.19━━━━━━━━━\u001b[0m \u001b[32m49,904/100,000 \u001b[0m [ \u001b[33m0:02:31\u001b[0m < \u001b[36m0:02:27\u001b[0m , \u001b[31m342 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m49,904/100,000 \u001b[0m [ \u001b[33m0:02:31\u001b[0m < \u001b[36m0:02:27\u001b[0m , \u001b[31m342 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------0m\u001b[90m━━━━━━━━━━━━\u001b[0m \u001b[32m49,904/100,000 \u001b[0m [ \u001b[33m0:02:31\u001b[0m < \u001b[36m0:02:27\u001b[0m , \u001b[31m342 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | 2.54     |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 50000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.972    |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00481  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 24       |\n",
      "|    policy_objective       | 0.0515   |\n",
      "|    std                    | 0.329    |\n",
      "|    value_loss             | 0.00237  |\n",
      "----------------------------------------\n",
      "\u001b[2KEval num_timesteps=60000, episode_reward=8.26 +/- 15.32━━━━━━━━\u001b[0m \u001b[32m59,954/100,000 \u001b[0m [ \u001b[33m0:03:04\u001b[0m < \u001b[36m0:02:00\u001b[0m , \u001b[31m336 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m59,954/100,000 \u001b[0m [ \u001b[33m0:03:04\u001b[0m < \u001b[36m0:02:00\u001b[0m , \u001b[31m336 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m59,954/100,000 \u001b[0m [ \u001b[33m0:03:04\u001b[0m < \u001b[36m0:02:00\u001b[0m , \u001b[31m336 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | 8.26     |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 60000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.983    |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00406  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 29       |\n",
      "|    policy_objective       | 0.0453   |\n",
      "|    std                    | 0.324    |\n",
      "|    value_loss             | 0.00165  |\n",
      "----------------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━\u001b[0m \u001b[32m59,954/100,000 \u001b[0m [ \u001b[33m0:03:04\u001b[0m < \u001b[36m0:02:00\u001b[0m , \u001b[31m336 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=70000, episode_reward=10.99 +/- 13.24━━━━━━━\u001b[0m \u001b[32m69,942/100,000 \u001b[0m [ \u001b[33m0:03:40\u001b[0m < \u001b[36m0:01:39\u001b[0m , \u001b[31m305 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m69,942/100,000 \u001b[0m [ \u001b[33m0:03:40\u001b[0m < \u001b[36m0:01:39\u001b[0m , \u001b[31m305 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------0m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m69,942/100,000 \u001b[0m [ \u001b[33m0:03:40\u001b[0m < \u001b[36m0:01:39\u001b[0m , \u001b[31m305 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | 11       |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 70000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.974    |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00602  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 34       |\n",
      "|    policy_objective       | 0.0549   |\n",
      "|    std                    | 0.319    |\n",
      "|    value_loss             | 0.00268  |\n",
      "----------------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━\u001b[0m \u001b[32m69,942/100,000 \u001b[0m [ \u001b[33m0:03:40\u001b[0m < \u001b[36m0:01:39\u001b[0m , \u001b[31m305 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=80000, episode_reward=10.52 +/- 9.8990m━━━━━\u001b[0m \u001b[32m79,956/100,000 \u001b[0m [ \u001b[33m0:04:16\u001b[0m < \u001b[36m0:01:07\u001b[0m , \u001b[31m302 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m79,956/100,000 \u001b[0m [ \u001b[33m0:04:16\u001b[0m < \u001b[36m0:01:07\u001b[0m , \u001b[31m302 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------[91m╸\u001b[0m\u001b[90m━━━━━\u001b[0m \u001b[32m79,956/100,000 \u001b[0m [ \u001b[33m0:04:16\u001b[0m < \u001b[36m0:01:07\u001b[0m , \u001b[31m302 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | 10.5     |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 80000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.97     |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00434  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 39       |\n",
      "|    policy_objective       | 0.0537   |\n",
      "|    std                    | 0.316    |\n",
      "|    value_loss             | 0.00159  |\n",
      "----------------------------------------\n",
      "\u001b[2KEval num_timesteps=90000, episode_reward=13.14 +/- 13.20\u001b[90m━━\u001b[0m \u001b[32m89,898/100,000 \u001b[0m [ \u001b[33m0:04:47\u001b[0m < \u001b[36m0:00:30\u001b[0m , \u001b[31m340 it/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m89,898/100,000 \u001b[0m [ \u001b[33m0:04:47\u001b[0m < \u001b[36m0:00:30\u001b[0m , \u001b[31m340 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m89,898/100,000 \u001b[0m [ \u001b[33m0:04:47\u001b[0m < \u001b[36m0:00:30\u001b[0m , \u001b[31m340 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | 13.1     |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 90000    |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.976    |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00468  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 43       |\n",
      "|    policy_objective       | 0.0678   |\n",
      "|    std                    | 0.31     |\n",
      "|    value_loss             | 0.0016   |\n",
      "----------------------------------------\n",
      "\u001b[2KNew best mean reward!━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m89,898/100,000 \u001b[0m [ \u001b[33m0:04:47\u001b[0m < \u001b[36m0:00:30\u001b[0m , \u001b[31m340 it/s\u001b[0m ]\n",
      "\u001b[2KEval num_timesteps=100000, episode_reward=12.93 +/- 11.57m \u001b[32m99,860/100,000 \u001b[0m [ \u001b[33m0:05:21\u001b[0m < \u001b[36m0:00:01\u001b[0m , \u001b[31m348 it/s\u001b[0m ]t/s\u001b[0m ]\n",
      "\u001b[2KEpisode length: 150.00 +/- 0.00━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m99,860/100,000 \u001b[0m [ \u001b[33m0:05:21\u001b[0m < \u001b[36m0:00:01\u001b[0m , \u001b[31m348 it/s\u001b[0m ]\n",
      "\u001b[2K----------------------------------------\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m99,860/100,000 \u001b[0m [ \u001b[33m0:05:21\u001b[0m < \u001b[36m0:00:01\u001b[0m , \u001b[31m348 it/s\u001b[0m ]\n",
      "| eval/                     |          |\n",
      "|    mean_ep_length         | 150      |\n",
      "|    mean_reward            | 12.9     |\n",
      "| time/                     |          |\n",
      "|    total_timesteps        | 100000   |\n",
      "| train/                    |          |\n",
      "|    explained_variance     | 0.98     |\n",
      "|    is_line_search_success | 1        |\n",
      "|    kl_divergence_loss     | 0.00437  |\n",
      "|    learning_rate          | 0.001    |\n",
      "|    n_updates              | 48       |\n",
      "|    policy_objective       | 0.044    |\n",
      "|    std                    | 0.297    |\n",
      "|    value_loss             | 0.00106  |\n",
      "----------------------------------------\n",
      "\u001b[2K\u001b[35m 100%\u001b[0m \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m100,352/100,000 \u001b[0m [ \u001b[33m0:05:21\u001b[0m < \u001b[36m0:00:00\u001b[0m , \u001b[31m290 it/s\u001b[0m ]t/s\u001b[0m ]\n",
      "\u001b[?25hSaving to logs/6_e/rlzoo3//trpo/ReacherBulletEnv-v0_1\n"
     ]
    }
   ],
   "source": [
    "!python -m rl_zoo3.train --algo trpo --env ReacherBulletEnv-v0 --progress \\\n",
    "--log-interval 400 --save-freq 10000 --eval-freq 10000 --eval-episodes 10 \\\n",
    "--n-timesteps 100000 \\\n",
    "--track --wandb-project-name trpo-ReacherBulletEnv-v0 -f logs/6_d/rlzoo3/"
   ]
  }
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